Bibliographic Collection
Data source: Clarivate Analytics Web of Science (http://apps.webofknowledge.com)
Data format: Plaintext
Query: TO = “Capacitated Arc Routing” OR “Capacitated General Routing”
Timespan: 2010-2019
Document Type: Articles, letters, review and proceedings papers
Query data: 12 May, 2019
Install and load bibliometrix R-package
# Stable version from CRAN (Comprehensive R Archive Network)
# if you need to execute the code, remove # from the beginning of the next line
# install.packages("bibliometrix")
# Most updated version from GitHub
# if you need to execute the code, remove # from the beginning of the next lines
# install.packages("devtools")
# devtools::install_github("massimoaria/bibliometrix")
library(bibliometrix)
## Warning: package 'bibliometrix' was built under R version 3.5.2
## To cite bibliometrix in publications, please use:
##
## Aria, M. & Cuccurullo, C. (2017) bibliometrix: An R-tool for comprehensive science mapping analysis, Journal of Informetrics, 11(4), pp 959-975, Elsevier.
##
##
## http:\\www.bibliometrix.org
##
##
## To start with the shiny web-interface, please digit:
## biblioshiny()
Data Loading and Converting
# Loading txt or bib files into R environment
D <- readFiles("../data/arp_grp_2010_2019_references.bib")
# Converting the loaded files into a R bibliographic dataframe
M <- convert2df(D, dbsource="isi",format="bibtex")
##
## Converting your isi collection into a bibliographic dataframe
##
## Articles extracted 100
## Articles extracted 200
## Articles extracted 237
## Done!
##
##
## Generating affiliation field tag AU_UN from C1: Done!
Section 1: Descriptive Analysis
Although bibliometrics is mainly known for quantifying the scientific production and measuring its quality and impact, it is also useful for displaying and analysing the intellectual, conceptual and social structures of research as well as their evolution and dynamical aspects.
In this way, bibliometrics aims to describe how specific disciplines, scientific domains, or research fields are structured and how they evolve over time. In other words, bibliometric methods help to map the science (so-called science mapping) and are very useful in the case of research synthesis, especially for the systematic ones.
Bibliometrics is an academic science founded on a set of statistical methods, which can be used to analyze scientific big data quantitatively and their evolution over time and discover information. Network structure is often used to model the interaction among authors, papers/documents/articles, references, keywords, etc.
Bibliometrix is an open-source software for automating the stages of data-analysis and data-visualization. After converting and uploading bibliographic data in R, Bibliometrix performs a descriptive analysis and different research-structure analysis.
Descriptive analysis provides some snapshots about the annual research development, the top “k” productive authors, papers, countries and most relevant keywords.
Main findings about the collection
#options(width=160)
results <- biblioAnalysis(M)
summary(results, k=10, pause=F, width=130)
Main Information about data
Documents 237
Sources (Journals, Books, etc.) 100
Keywords Plus (ID) 207
Author's Keywords (DE) 421
Period 2000 - 2019
Average citations per documents 16.09
Authors 368
Author Appearances 745
Authors of single-authored documents 11
Authors of multi-authored documents 357
Single-authored documents 12
Documents per Author 0.644
Authors per Document 1.55
Co-Authors per Documents 3.14
Collaboration Index 1.59
Document types
ARTICLE 154
ARTICLE, BOOK CHAPTER 9
ARTICLE, DATA PAPER 1
ARTICLE, PROCEEDINGS PAPER 21
PROCEEDINGS PAPER 52
Annual Scientific Production
Year Articles
2000 3
2001 7
2002 2
2003 9
2004 6
2005 10
2006 8
2007 6
2008 15
2009 13
2010 19
2011 10
2012 10
2013 13
2014 28
2015 17
2016 24
2017 12
2018 17
2019 8
Annual Percentage Growth Rate 5.297827
Most Productive Authors
Authors Articles Authors Articles Fractionalized
1 PRINS C 20 PRINS C 7.20
2 CORBERAN A 17 CORBERAN A 5.10
3 YAO X 17 YAO X 5.08
4 SANCHIS JM 13 WOHLK S 4.50
5 MEI Y 12 LAPORTE G 4.25
6 TANG K 12 SANCHIS JM 3.82
7 LAPORTE G 11 MEI Y 3.75
8 LAGANA D 10 TANG K 3.67
9 LACOMME P 9 LACOMME P 2.83
10 BENAVENT E 8 LAGANA D 2.58
Top manuscripts per citations
Paper TC TCperYear
1 HERTZ A, 2000, OPER RES 140 7.37
2 LACOMME P, 2004, ANN OPER RES 131 8.73
3 BEULLENS P, 2003, EUR J OPER RES 111 6.94
4 TANG K, 2009, IEEE TRANS EVOL COMPUT 98 9.80
5 MEI Y, 2011, IEEE TRANS EVOL COMPUT 96 12.00
6 BRANDAO J, 2008, COMPUT OPER RES 88 8.00
7 LONGO H, 2006, COMPUT OPER RES 85 6.54
8 BELENGUER JM, 2003, COMPUT OPER RES 83 5.19
9 GREISTORFER P, 2003, COMPUT IND ENG 66 4.12
10 LACOMME P, 2001, APPLICATIONS OF EVOLUTIONARY COMPUTING, PROCEEDINGS 66 3.67
Corresponding Author's Countries
Country Articles Freq SCP MCP MCP_Ratio
1 CHINA 35 0.1477 18 17 0.486
2 SPAIN 31 0.1308 16 15 0.484
3 FRANCE 27 0.1139 24 3 0.111
4 ITALY 17 0.0717 10 7 0.412
5 CANADA 14 0.0591 10 4 0.286
6 PORTUGAL 13 0.0549 7 6 0.462
7 BRAZIL 11 0.0464 11 0 0.000
8 GERMANY 9 0.0380 9 0 0.000
9 UNITED KINGDOM 9 0.0380 6 3 0.333
10 AUSTRALIA 8 0.0338 4 4 0.500
SCP: Single Country Publications
MCP: Multiple Country Publications
Total Citations per Country
Country Total Citations Average Article Citations
1 FRANCE 627 23.22
2 SPAIN 552 17.81
3 CHINA 520 14.86
4 PORTUGAL 279 21.46
5 CANADA 265 18.93
6 SWITZERLAND 204 102.00
7 ITALY 197 11.59
8 BRAZIL 187 17.00
9 BELGIUM 151 50.33
10 AUSTRIA 136 45.33
Most Relevant Sources
Sources Articles
1 COMPUTERS \\& OPERATIONS RESEARCH 29
2 EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 26
3 NETWORKS 13
4 JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 7
5 OPERATIONS RESEARCH 7
6 ARC ROUTING: PROBLEMS METHODS AND APPLICATIONS 6
7 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 6
8 MATHEMATICAL PROGRAMMING 6
9 TRANSPORTATION SCIENCE 6
10 ANNALS OF OPERATIONS RESEARCH 4
Most Relevant Keywords
Author Keywords (DE) Articles Keywords-Plus (ID) Articles
1 CAPACITATED ARC ROUTING PROBLEM 52 ALGORITHM 60
2 ARC ROUTING 36 ALGORITHMS 31
3 HEURISTICS 21 RURAL POSTMAN PROBLEM 28
4 VEHICLE ROUTING 15 OPTIMIZATION 26
5 MEMETIC ALGORITHM 13 SEARCH 24
6 COMBINATORIAL OPTIMIZATION 11 BOUNDS 20
7 CAPACITATED ARC ROUTING 10 TABU SEARCH ALGORITHM 18
8 CARP 10 GENERAL ROUTING PROBLEM 16
9 LOCAL SEARCH 10 INEQUALITIES 16
10 ROUTING 10 POLYHEDRON 16
plot(x=results, k=10, pause=F)
Warning: Removed 1 rows containing missing values (position_stack).
Warning: Removed 1 rows containing missing values (geom_path).
Most Cited References
CR <- citations(M, field = "article", sep = ";")
cbind(CR$Cited[1:20])
[,1]
GOLDEN BL, 1981, NETWORKS, V11, P305, DOI 10.1002/NET.3230110308. 144
GOLDEN BL, 1983, COMPUT OPER RES, V10, P47, DOI 10.1016/0305-0548(83)90026-6. 100
HERTZ A, 2000, OPER RES, V48, P129, DOI 10.1287/OPRE.48.1.129.12455. 88
LACOMME P, 2004, ANN OPER RES, V131, P159, DOI 10.1023/B:ANOR.0000039517.35989.6D. 88
BEULLENS P, 2003, EUR J OPER RES, V147, P629, DOI 10.1016/S0377-2217(02)00334-X. 82
BENAVENT E, 1992, NETWORKS, V22, P669, DOI 10.1002/NET.3230220706. 78
DROR M., 2000, ARC ROUTING THEORY S. 74
BELENGUER JM, 2003, COMPUT OPER RES, V30, P705, DOI 10.1016/S0305-0548(02)00046-1. 71
ULUSOY G, 1985, EUR J OPER RES, V22, P329, DOI 10.1016/0377-2217(85)90252-8. 71
BRANDAO J, 2008, COMPUT OPER RES, V35, P1112, DOI 10.1016/J.COR.2006.07.007. 70
EGLESE RW, 1994, DISCRETE APPL MATH, V48, P231, DOI 10.1016/0166-218X(92)00003-5. 60
LONGO H, 2006, COMPUT OPER RES, V33, P1823, DOI 10.1016/J.COR.2004.11.020. 56
EISELT HA, 1995, OPER RES, V43, P399, DOI 10.1287/OPRE.43.3.399. 48
TANG K, 2009, IEEE T EVOLUT COMPUT, V13, P1151, DOI 10.1109/TEVC.2009.2023449. 48
LI LYO, 1996, J OPER RES SOC, V47, P217, DOI 10.1057/JORS.1996.20. 47
HERTZ A, 2001, TRANSPORT SCI, V35, P425, DOI 10.1287/TRSC.35.4.425.10431. 45
BELENGUER JM, 2006, COMPUT OPER RES, V33, P3363, DOI 10.1016/J.COR.2005.02.009. 44
LACOMME P, 2001, LECT NOTES COMPUT SC, V2037, P473. 42
BALDACCI R, 2006, NETWORKS, V47, P52, DOI [10.1002/NET.20091, 10.1002/NET.20091]. 41
HIRABAYASHI R, 1992, ASIA PAC J OPER RES, V9, P155. 38
Section 2: The Intellectual Structure of the field - Co-citation Analysis
Citation analysis is one of the main classic techniques in bibliometrics. It shows the structure of a specific field through the linkages between nodes (e.g. authors, papers, journal), while the edges can be differently interpretated depending on the network type, that are namely co-citation, direct citation, bibliographic coupling. Please see Aria, Cuccurullo (2017).
Below there are three examples.
First, a co-citation network that shows relations between cited-reference works (nodes).
Second, a co-citation network that uses cited-journals as unit of analysis.
The useful dimensions to comment the co-citation networks are: (i) centrality and peripherality of nodes, (ii) their proximity and distance, (iii) strength of ties, (iv) clusters, (iiv) bridging contributions.
Third, a historiograph is built on direct citations. It draws the intellectual linkages in a historical order. Cited works of thousands of authors contained in a collection of published scientific articles is sufficient for recostructing the historiographic structure of the field, calling out the basic works in it.
Article (References) co-citation analysis
Plot options:
n = 50 (the funxtion plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to their degree)
size = 20 (the max size of vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 0.7 (defines the size of vertex labels)
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
edges.min = 5 (plots only edges with a strength greater than or equal to 5)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "references", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)
Descriptive analysis of Article co-citation network characteristics
#netstat <- networkStat(NetMatrix)
#summary(netstat,k=10)
Journal (Source) co-citation analysis
M=metaTagExtraction(M,"CR_SO",sep=";")
NetMatrix <- biblioNetwork(M, analysis = "co-citation", network = "sources", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Co-Citation Network", type = "auto", size.cex=TRUE, size=15, remove.multiple=FALSE, labelsize=0.7,edgesize = 10, edges.min=5)
Descriptive analysis of Journal co-citation network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 1299
Density 0.041
Transitivity 0.216
Diameter 3
Degree Centralization 0.87
Average path length 1.984
Section 3: Historiograph - Direct citation linkages
histResults <- histNetwork(M, min.citations=quantile(M$TC,0.75), sep = ";")
## Articles analysed 63
options(width = 130)
net <- histPlot(histResults, n=20, size.cex=TRUE, size = 5, labelsize = 3, arrowsize = 0.5)
Legend
Paper DOI Year LCS GCS
2000 - 1 AMBERG A, 2000, EUR J OPER RES 10.1016/S0377-2217(99)00170-8 2000 23 46
2000 - 2 HERTZ A, 2000, OPER RES 10.1287/OPRE.48.1.129.12455 2000 88 140
2001 - 4 GHIANI G, 2001, NETWORKS 10.1002/NET.3 2001 24 41
2001 - 5 CORBERAN A, 2001, MATH PROGRAM 10.1007/PL00011426 2001 23 32
2003 - 10 BEULLENS P, 2003, EUR J OPER RES 10.1016/S0377-2217(02)00334-X 2003 82 111
2003 - 12 BELENGUER JM, 2003, COMPUT OPER RES 10.1016/S0305-0548(02)00046-1 2003 71 83
2003 - 14 GREISTORFER P, 2003, COMPUT IND ENG 10.1016/S0360-8352(02)00178-X 2003 33 66
2004 - 15 LACOMME P, 2004, ANN OPER RES 10.1023/B:ANOR.0000039517.35989.6D 2004 88 131
2005 - 18 CHU F, 2005, EUR J OPER RES 10.1016/J.EJOR.2004.08.017 2005 27 55
2005 - 19 LACOMME P, 2005, EUR J OPER RES 10.1016/J.EJOR.2004.04.021 2005 28 58
2006 - 24 BELENGUER JM, 2006, COMPUT OPER RES 10.1016/J.COR.2005.02.009 2006 44 64
2006 - 25 WOHLK S, 2006, COMPUT OPER RES 10.1016/J.COR.2005.02.015 2006 18 20
2006 - 26 LACOMME P, 2006, COMPUT OPER RES 10.1016/J.COR.2005.02.017 2006 25 64
2006 - 27 LONGO H, 2006, COMPUT OPER RES 10.1016/J.COR.2004.11.020 2006 56 85
2006 - 28 BALDACCI R, 2006, NETWORKS 10.1002/NET.20091 2006 41 57
2008 - 33 POLACEK M, 2008, J HEURISTICS 10.1007/S10732-007-9050-2 2008 37 60
2008 - 35 BRANDAO J, 2008, COMPUT OPER RES 10.1016/J.COR.2006.07.007 2008 70 88
2009 - 42 LETCHFORD AN, 2009, COMPUT OPER RES 10.1016/J.COR.2008.09.008 2009 22 28
2010 - 46 CORBERAN A, 2010, NETWORKS 10.1002/NET.20347 2010 35 54
2010 - 48 GOUVEIA L, 2010, COMPUT OPER RES 10.1016/J.COR.2009.06.018 2010 19 25
Section 4: The conceptual structure - Co-Word Analysis
Co-word networks show the conceptual structure, that uncovers links between concepts through term co-occurences.
Conceptual structure is often used to understand the topics covered by scholars (so-called research front) and identify what are the most important and the most recent issues.
Dividing the whole timespan in different timeslices and comparing the conceptual structures is useful to analyze the evolution of topics over time.
Bibliometrix is able to analyze keywords, but also the terms in the articles’ titles and abstracts. It does it using network analysis or correspondance analysis (CA) or multiple correspondance analysis (MCA). CA and MCA visualise the conceptual structure in a two-dimensional plot.
Co-word Analysis through Keyword co-occurrences
Plot options:
normalize = “association” (the vertex similarities are normalized using association strength)
n = 50 (the function plots the main 50 cited references)
type = “fruchterman” (the network layout is generated using the Fruchterman-Reingold Algorithm)
size.cex = TRUE (the size of the vertices is proportional to their degree)
size = 20 (the max size of the vertices)
remove.multiple=FALSE (multiple edges are not removed)
labelsize = 3 (defines the max size of vertex labels)
label.cex = TRUE (The vertex label sizes are proportional to their degree)
edgesize = 10 (The thickness of the edges is proportional to their strength. Edgesize defines the max value of the thickness)
label.n = 30 (Labels are plotted only for the main 30 vertices)
edges.min = 25 (plots only edges with a strength greater than or equal to 2)
all other arguments assume the default values
NetMatrix <- biblioNetwork(M, analysis = "co-occurrences", network = "keywords", sep = ";")
net=networkPlot(NetMatrix, normalize="association", n = 50, Title = "Keyword Co-occurrences", type = "fruchterman", size.cex=TRUE, size=20, remove.multiple=F, edgesize = 10, labelsize=3,label.cex=TRUE,label.n=30,edges.min=2)
Descriptive analysis of keyword co-occurrences network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=10)
Main statistics about the network
Size 210
Density 0.055
Transitivity 0.346
Diameter 6
Degree Centralization 0.29
Average path length 2.553
Section 5: Thematic Map
Co-word analysis draws clusters of keywords. They are considered as themes, whose density and centrality can be used in classifying themes and mapping in a two-dimensional diagram.
Thematic map is a very intuitive plot and we can analyze themes according to the quadrant in which they are placed: (1) upper-right quadrant: motor-themes; (2) lower-right quadrant: basic themes; (3) lower-left quadrant: emerging or disappearing themes; (4) upper-left quadrant: very specialized/niche themes.
Please see Cobo, M. J., L?pez-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146-166.
Map=thematicMap(M, field = "ID", n = 250, minfreq = 5,
stemming = FALSE, size = 0.5, repel = TRUE)
plot(Map$map)
Cluster description
Clusters=Map$words[order(Map$words$Cluster,-Map$words$Occurrences),]
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
CL <- Clusters %>% group_by(.data$Cluster_Label) %>% top_n(5, .data$Occurrences)
CL
## # A tibble: 22 x 5
## # Groups: Cluster_Label [6]
## Occurrences Words Cluster Color Cluster_Label
## <dbl> <chr> <dbl> <chr> <chr>
## 1 26 optimization 1 #E41A1C optimization
## 2 7 design 1 #E41A1C optimization
## 3 9 chinese postman problem 2 #377EB8 chinese postman problem
## 4 8 memetic algorithm 2 #377EB8 chinese postman problem
## 5 31 algorithms 3 #4DAF4A algorithms
## 6 24 search 3 #4DAF4A algorithms
## 7 8 postman problem 3 #4DAF4A algorithms
## 8 7 arc routing problem 3 #4DAF4A algorithms
## 9 7 arc routing-problems 3 #4DAF4A algorithms
## 10 15 time windows 4 #FF7F00 time windows
## # ... with 12 more rows
Section 6: The social structure - Collaboration Analysis
Collaboration networks show how authors, institutions (e.g. universities or departments) and countries relate to others in a specific field of research. For example, the first figure below is a co-author network. It discovers regular study groups, hidden groups of scholars, and pivotal authors. The second figure is called “Edu collaboration network” and uncovers relevant institutions in a specific research field and their relations.
Author collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "authors", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Author collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)
Descriptive analysis of author collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 368
Density 0.01
Transitivity 0.524
Diameter 8
Degree Centralization 0.047
Average path length 3.296
Edu collaboration network
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "universities", sep = ";")
net=networkPlot(NetMatrix, n = 50, Title = "Edu collaboration",type = "auto", size=10,size.cex=T,edgesize = 3,labelsize=0.6)
Descriptive analysis of edu collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 192
Density 0.011
Transitivity 0.401
Diameter 7
Degree Centralization 0.057
Average path length 3.13
Country collaboration network
M <- metaTagExtraction(M, Field = "AU_CO", sep = ";")
NetMatrix <- biblioNetwork(M, analysis = "collaboration", network = "countries", sep = ";")
net=networkPlot(NetMatrix, n = dim(NetMatrix)[1], Title = "Country collaboration",type = "sphere", size=10,size.cex=T,edgesize = 1,labelsize=0.6, cluster="none")
Descriptive analysis of country collaboration network characteristics
netstat <- networkStat(NetMatrix)
summary(netstat,k=15)
Main statistics about the network
Size 41
Density 0.073
Transitivity 0.278
Diameter 4
Degree Centralization 0.302
Average path length 2.363
